Dynamic modeling of ball screw feed systems: A review and framework from physical mechanisms to a physics–data hybrid approach

  • Yongqiang Li orcid

    Faculty of Innovative Design and Technology, Universiti Sultan Zainal Abidin, Kuala Nerus 21300, Malaysia;  School of Intelligent Equipment, Lanzhou University of Information Science and Technology, Lanzhou 730300, China
  • Saiful Bahri Bin Mohamed orcid

    Faculty of Innovative Design and Technology, Universiti Sultan Zainal Abidin, Kuala Nerus 21300, Malaysia
  • Mohd Shahir Bin Kasim orcid

    Faculty of Innovative Design and Technology, Universiti Sultan Zainal Abidin, Kuala Nerus 21300, Malaysia
  • Siti Nurul Akmal Binti Yusof orcid

    Faculty of Innovative Design and Technology, Universiti Sultan Zainal Abidin, Kuala Nerus 21300, Malaysia
Article ID: 4089
DOI: https://doi.org/10.59400/sv4089
Keywords: ball screw feed system, nonlinear time-varying dynamics, multiphysics coupling, position-dependent stiffness, physics-informed hybrid modeling

Abstract

Ball screw feed systems demonstrate that pronounced nonlinearity, time variation, and multiphysics coupling arise under high speed, high acceleration, and long-duration operation, where neither purely physics-based nor purely data-driven models can simultaneously ensure interpretability, deployability, and cross-condition reliability. However, the significant challenges this presents show that a more integrated framework could address these limitations more effectively. Moreover, the findings from existing approaches indicate that a Coupled Mechanism–Model–Data (CMMD) framework could provide a systematic way to connect physical mechanisms with application-oriented modeling through a hierarchical and constrained architecture. In light of these key results, the evidence shows that following a unified logic of "mechanism coupling–model structure–data updating interface" appears to support more coherent analytical development. Research shows friction, stiffness, thermal deformation, and wear affect modal properties. Nevertheless, the important propagation effects indicate that formulating these as identifiable nonlinear and time-varying terms demonstrates that the framework retains critical physical interpretability. Furthermore, the evidence shows that hierarchical physics-based model families appear to differ significantly in representational capability, computational cost, and boundary sensitivity. Given that the findings demonstrate that conditions involving strong nonlinearity and parameter drift present particular analytical difficulty, LPV–NARX (Linear Parameter-Varying Nonlinear AutoRegressive model with eXogenous inputs), sparse identification, neural networks, and three hybrid paradigms—residual compensation, adaptive parameter updating, and physics-constrained learning—indicate that synthesis according to fusion location, updating objects, and failure boundaries could establish a constraint-driven hierarchical selection strategy. Data shows hybrid paradigms yield representative application scenarios.

Published
2026-05-27
How to Cite
Li, Y., Saiful Bahri Bin Mohamed, Mohd Shahir Bin Kasim, & Siti Nurul Akmal Binti Yusof. (2026). Dynamic modeling of ball screw feed systems: A review and framework from physical mechanisms to a physics–data hybrid approach. Sound & Vibration, 6(3). https://doi.org/10.59400/sv4089
Issue
Vol. 60 No. 3 (2026): In Progress
Section
Article

Copyright (c) 2026 Yongqiang Li, Saiful Bahri Bin Mohamed, Mohd Shahir Bin Kasim, Siti Nurul Akmal Binti Yusof

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

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